In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality. One very common set of input documents includes color photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, arid not a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
In digital image processing, three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image, but its output documents are sometimes not satisfactory to the ultimate customer.
In a second case of image enhancement, the image can always be processed. It turns out than an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further, the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved, thereby allowing a stable process.
One common deficiency of images is the apparent lack of image saturation, or, interchangeably, the desire to obtain a higher image saturation. Several factors contribute to the lack of image saturation. One factor is the degradation of images due to aging or other external influences. Another factor is the mismatch between data range of the image and the possible dynamic range of the scanning system. A third factor is the discrepancy between the "memory" of an image and the "reality" of an image. A common example for this third category is the desire for images to display "blue sky" and "green grass" even if this does not accurately reproduce the colors or the colorfulness of the original scene.
One approach to correct the saturation of a color image is described by Akira Inoue and Johji Tajima in "Adaptive Quality Improvement Method for Color Images," IS&T/SPIE Symposium on Electronic Imaging Science and Technology, [2179-43], 1994. In this approach the maximum saturation in HSV (hue-saturation-value) color space is measured and a correction coefficient is calculated to adjust the maximum measured saturation Smax to be transformed into the maximum obtainable saturation Smax=1. The difficulty of this approach is that false measurements are easily obtained due to the unreliable nature of the HSV data for low V values. Secondly, relying on one single measurement makes the algorithm susceptible to image noise. Thirdly adjusting image saturation in HSV space can lead to strong image noise in the visual appearance of lightness or luminance.
The references cited herein, and the listed co-pending patent applications are herein incorporated by reference for their teachings.